In Generative AI: Zero-Shot and Few-Shot

Phani Monogya Katikireddi and Santosh Jaini

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022, vol. 8, issue 1, 391-397

Abstract: Generative AI has become a change-maker in many fields, using different text, image, and voice generation modes. One of the profound sub-areas within this domain is the optimum utilization of learning systems with minimal information using zero- and few-shot learning. Zero-shot learning lets models operate on novel classes or tasks for which it has no training sample, while few-shot learning allows models to learn with initial samples. Such approaches are useful when it is difficult or expensive to obtain information, which suggests a technique for providing a direction for developing accurate AI models when data are lacking. This paper explains the background, application, and challenges of generative AI models that use zero-shot/one-shot learning, outlining how these techniques help set new paradigms and raise innovative horizons for AI systems.

Keywords: Generative AI; Zero-Shot Learning; Few-Shot Learning; Data Scarcity; Model Generalization; Transfer Learning; Meta-Learning; Advanced Architectures; Transformers; Real-Time Applications (search for similar items in EconPapers)
Date: 2022
Note: Article URL: https://ijsrcseit.com/CSEIT2390668
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v8:y2022:i1:id:hcseit2390668

DOI: 10.32628/CSEIT2390668

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